Search

This work considers targeted maximum likelihood estimation (TMLE) of treatment effects on absolute risk and survival probabilities in classical time‐to‐event settings characterized by right‐censoring and competing risks. TMLE is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two‐step procedure for substitution estimation of causal parameters. We specialize and extend the continuous‐time TMLE methods for competing risks settings, proposing a targeting algorithm that iteratively updates cause‐specific hazards to solve the efficient influence curve equation for the target parameter. As part of the work, we further detail and implement the recently proposed highly adaptive lasso estimator for continuous‐time conditional hazards with L1‐penalized Poisson regression. The resulting estimation procedure benefits from relying solely on very mild nonparametric restrictions on the statistical model, thus providing a novel tool for machine‐learning‐based semiparametric causal inference for continuous‐time time‐to‐event data. We apply the methods to a publicly available dataset on follicular cell lymphoma where subjects are followed over time until disease relapse or death without relapse. The data display important time‐varying effects that can be captured by the highly adaptive lasso. In our simulations that are designed to imitate the data, we compare our methods to a similar approach based on random survival forests and to the discrete‐time TMLE. [ABSTRACT FROM AUTHOR]

Summary: Recurrent events in the presence of a terminal event are often encountered in a biomedical setting. The marginal mean of the number of recurrent events in a specified time period is a useful non‐parametric summary of recurrent events data also in the presence of a terminal event. Other useful non‐parametric summaries, that are simple to compute, are the distribution function of the number of recurrent events for each point in time and the variance of the number of recurrent events. For bivariate recurrent events, still in the presence of a terminal event, we suggest a simple non‐parametric estimator of the covariance or correlation of the marginal number of events for both processes. When there is no terminal event the correlation is useful, but when there is an important terminal event we suggest an adjustment for correlation induced by the terminal event to obtain a measure that reflects the dependence in the recurrent event processes among survivors only. Our estimators can be used for deciding whether the two recurrent events are correlated and in what way. We provide large sample properties of our estimators and show their performance in small samples by simulations. The estimators are applied in a study of catheter complications among patients receiving home parenteral nutrition through a central venous catheter, and we show a positive correlation between the number of infections and the number of occlusion defects. [ABSTRACT FROM AUTHOR]

Background Patients with end-stage renal disease (ESRD) have high morbidity and mortality rates, with cardiovascular diseases and infections being the major causes of death. Mannose-binding lectin (MBL) has been suggested to play a protective role in this regard. The aim of this study was to investigate a possible clinical association of MBL genotypes (MBL2) with outcome among patients on dialysis or with a functioning graft. Methods A total of 98 patients with ESRD accepted for living-donor renal transplantation or on the waiting list for transplantation were included and prospectively followed for an average of 9 years (range 7.5–9.9). Medical records were evaluated regarding transplantation status, diabetes mellitus, vascular parameters and infections for all the patients. Cox regression models and logistic regression analysis were used for statistical analyses. The cohort was divided into two groups according to the MBL2 genotype (normal A/A versus variant A/O or O/O). Results We found no evidence for an association between the MBL2 genotype and all-cause mortality, cardiovascular events or bacterial infections (pneumonia, urinary tract infection, fistula infection or other infections). Conclusion In this cohort, the MBL2 genotype did not seem to be associated with any long-term clinical effects in ESRD patients on dialysis or with a functioning graft. [ABSTRACT FROM AUTHOR]

We suggest an estimator for the proportional odds cumulative incidence model for competing risks data. The key advantage of this model is that the regression parameters have the simple and useful odds ratio interpretation. The model has been considered by many authors, but it is rarely used in practice due to the lack of reliable estimation procedures. We suggest such procedures and show that their performance improve considerably on existing methods. We also suggest a goodness-of-fit test for the proportional odds assumption. We derive the large sample properties and provide estimators of the asymptotic variance. The method is illustrated by an application in a bone marrow transplant study and the finite-sample properties are assessed by simulations. [ABSTRACT FROM AUTHOR]

Epidemiological studies of related individuals are often complicated by the fact that follow-up on the event type of interest is incomplete due to the occurrence of other events. We suggest a class of frailty models with cause-specific hazards for correlated competing events in related individuals. The frailties are based on sums of gamma distributed variables and offer closed form expressions for the observed intensities. An inference procedure with a recursive baseline estimator is proposed, and its large sample properties are established. The estimator readily handles cluster left-truncation as occurring in the Nordic twin registers. The performance in finite samples is investigated by simulations and an example on prostate cancer in twins is provided for illustration. [ABSTRACT FROM AUTHOR]

Background Children and adolescents are more prone to develop adverse effects during antipsychotic treatment than adults. The purpose of this paper iso investigate cardiometabolic effects and its predictors in youth with first-episode psychosis (FEP) treated with quetiapine-extended release (ER) vs. aripiprazole. Methods Youths with FEP aged 12–17 years were randomized to quetiapine-ER or aripiprazole in the 12-week, double-blinded, Tolerability and Efficacy of Antipsychotics (TEA) trial. Primary outcome was change in body weight; secondary outcomes were changes in body mass index (BMI) and waist circumference (WC), blood pressure (BP), heart rate, and lipid and glucose metabolism parameters. Possible predictors of cardiometabolic changes were examined. Results Altogether, 113 patients (schizophrenia-spectrum disorders=93%, age (mean±SD): 15.7±1.4 years, males=30.1%), were randomized to quetiapine-ER (n=55) or aripiprazole (n=58). Quetiapine-ER was associated with significantly larger increases in body weight, BMI z-score and WC z-score than aripiprazole (all between-group p-values p<0.0001): Lipid and glucose metabolism parameters increased significantly at week 4 and 12 only with quetiapine-ER (p-range: 0.0001–0.037). Quetiapine-ER was associated with an increased occurrence of obesity, elevated blood lipids and hyperinsulinemia (p-range=0.004–0.039). Early weight gain, obesity or type 2 diabetes in the family significantly predicted weight and BMI gain at week 12. Discussion In youth with FEP, quetiapine-ER was associated with significantly greater weight gain and adverse changes in metabolic outcomes than aripiprazole. Early weight gain must be addressed and family lifestyle factors taken into consideration when treating youth with antipsychotics. [ABSTRACT FROM AUTHOR]